
SumProduct Graphical Models
This paper introduces a new probabilistic architecture called SumProduc...
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Investigation of commuting Hamiltonian in quantum Markov network
Graphical Models have various applications in science and engineering wh...
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Lifted Graphical Models: A Survey
This article presents a survey of work on lifted graphical models. We re...
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Combinatorial Inference for Graphical Models
We propose a new family of combinatorial inference problems for graphica...
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Connecting actuarial judgment to probabilistic learning techniques with graph theory
Graphical models have been widely used in applications ranging from medi...
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Mixtures of DeterministicProbabilistic Networks and their AND/OR Search Space
The paper introduces mixed networks, a new framework for expressing and ...
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Learning Tractable Probabilistic Models in Open Worlds
Largescale probabilistic representations, including statistical knowled...
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Structure Learning of Probabilistic Graphical Models: A Comprehensive Survey
Probabilistic graphical models combine the graph theory and probability theory to give a multivariate statistical modeling. They provide a unified description of uncertainty using probability and complexity using the graphical model. Especially, graphical models provide the following several useful properties:  Graphical models provide a simple and intuitive interpretation of the structures of probabilistic models. On the other hand, they can be used to design and motivate new models.  Graphical models provide additional insights into the properties of the model, including the conditional independence properties.  Complex computations which are required to perform inference and learning in sophisticated models can be expressed in terms of graphical manipulations, in which the underlying mathematical expressions are carried along implicitly. The graphical models have been applied to a large number of fields, including bioinformatics, social science, control theory, image processing, marketing analysis, among others. However, structure learning for graphical models remains an open challenge, since one must cope with a combinatorial search over the space of all possible structures. In this paper, we present a comprehensive survey of the existing structure learning algorithms.
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